Oil field operators dedicate significant resources to developing tools that help improve the overall production of oil and gas wells. Among such tools are computer-based models used to simulate the behavior of the fluids within a reservoir (e.g., water, oil and natural gas). These models enable operators to predict and optimize future production of the field as fluids are extracted and the field is depleted. To help ensure the accuracy of such predictions, the wells are periodically logged using production logging tools to update and maintain a historical database of relevant metrics for the wells within a field. Simulation model results may then be regularly correlated against the updated historical data, with modeling parameters being adjusted as needed to reduce the error between simulated and actual values.
As part of the process of modeling a reservoir, regions such as the stratigraphic layers and facies within the reservoir formations are each subdivided or “blocked” into smaller discrete modeling units or “grid cells”, each of which is individually evaluated for each simulation time interval. This “well-blocking” enables the upscaling or downscaling of borehole properties to match the scale of the desired grid for the simulation model at the borehole location. Upscaling generally refers to generating coarser resolution values from finer resolution samples, while downscaling generally refers to generating finer resolution values from coarser resolution samples. Well log curves providing the parameter values are selected together with the corresponding destination grid from the model. The destination grid typically dictates the degree of upscaling or downscaling needed.
For applications that employ continuous parameters (e.g., porosity and permeability), various methods may be used to aggregate or select a single value to assign to each grid cell along the borehole, ranging from simple averaging to stochastic sampling (e.g., Monte Carlo methods). For applications that employ discrete parameters that are either nominal or ordinal (e.g., facies or rock types coded by integer values), a single value is selected for the grid cell that may be based, for example, upon a frequency of occurrence, a random selection or a deterministic calculation of parameter values corresponding to the grid cell. The results of the well blocking provides a starting point for interpolation and/or simulation, which in turn provides a basis for population the remainder of the grid cells beyond each of the blocked borehole locations. The choice of cell size thus can significantly affect the simulation output and its accuracy, i.e., how well the simulation tracks with actual data. If the cell sizes are set too large, significant variations that take place over a narrow regions (e.g., over a narrow well borehole depth range), may not be accurately predicted by the model. If the cell sizes are set to small, the computational load may become excessive and lead to unacceptably long simulation run times.
It should be understood that the drawings and corresponding detailed description do not limit the disclosure, but on the contrary, they provide the foundation for understanding all modifications, equivalents, and alternatives falling within the scope of the appended claims.